We invite two types of original and unpublished works: Long papers (8 pages) should describe solid results with strong experimental, empirical or theoretical/formal backing, short papers (4 pages) should describe work in progress where preliminary results have already been worked out. Accepted papers will appear in the workshop proceedings. All papers are allowed unlimited but a reasonable number of pages for references. Final camera-ready versions will be allowed an additional page of content to address reviewers’ comments. All submissions must be anonymized, in PDF format (using the LREC-COLING 2024 style sheets for the main conference) and must be made through the Softconf website set up for this workshop (will be opened soon).
When submitting a paper from the START page, authors will be asked to provide essential information about resources (in a broad sense, i.e. also technologies, standards, evaluation kits, etc.) that have been used for the work described in the paper or are a new result of your research. Moreover, ELRA encourages all LREC-COLING authors to share the described LRs (data, tools, services, etc.) to enable their reuse and replicability of experiments (including evaluation ones).
Topics of interest for the workshop include, but are not limited to the following themes:
- NLP-based (stock) market analytics, e.g., prediction of economic performance indicators (trend prediction, performance forecasting, etc.), by analyzing verbal statements of enterprises, businesses, companies, and associated legal or administrative actors
- NLP-based product analytics, e.g., based on social and mass media monitoring, summarizing reviews, classifying and mining complaint messages and other (non)verbal types of customer reactions to products or services
- NLP-based customer analytics, e.g., client profiling, tracking product/company preferences, screening customer reviews or complaints
- NLP-based organization/enterprise analytics (e.g., risk prediction, fraud analysis, predictive analysis of annual business, analysis of financial and corporate social responsibility reports, etc.)
- NLP-based ESG-analytics, e.g., information extraction and sentiment analysis of Environmental, Social and Governance-related text and social media posts
- NLP-based analysis of macro-economic phenomena in which national economies and the (inter)national banking system (IMF, Fed, PBoC, ECB) play an influential role
- Market sentiments and emotions as evident from consumers’ and enterprises’ verbal behavior and their communication strategies about products and services
- Relationship and interaction between quantitative (structured) economic data (e.g., contained in time series data) and qualitative (unstructured verbal) economic data (press releases, newswire streams, social media contents, conference call statements, etc.)
- Credibility and trust models for business agents involved in economic processes (e.g., as traders, sellers, advertisers) extracted from legacy data of their communication behavior
- Deceptive or fake information recognition (fact or claim checking) related to economic objects (such as products, advertisements, etc.) or economic actors (such as industries, companies, reviewers, etc.), including opinion spam targeting at or emanating from economic actors and processes
- Verbally fluent software agents (chatbots for sales and marketing) as virtual actors in economic processes, e.g., embodying models of persuasion, information biases, (un)fair trading
- Client-supplier interaction platforms (e.g., portals, helps desks, newsgroups) and transaction support systems based on written or spoken natural language communication
- Information aggregation of economic data and opinion statements from large, heterogeneous sources (e.g., generation of review or meeting summaries, automatic threading of social media communication)
- Economy-tuned language models (domain adaptation policies, prompting strategies, etc.), • Text generation in economic domains, e.g., review generation, complaint response generation
- Generation and maintenance of knowledge graphs and ontologies for economics
- Corpora and annotation policies (guidelines, metadata schemata, etc.) for economic NLP
